1. Field of the Invention
The present invention relates to a method for measuring the dynamics of a flow of energy, as well as to the associated device and system. In particular, the method concerns the observation and analytical measurement of a flow of energy and/or fluids in motion, such as, for example, gas, water or another form or substance.
2. Present State of the Art
Cyber Physical Systems, or CPS, are known in the art, which comprise physical entities that carry out a given function and computational elements that control said physical entities (see Edward A. Lee. “Cyber Physical Systems: Design Challenges” in ISORC, pages 363-369, 2008). One example of such CPS systems is the intelligent distribution network for electric energy, gas, water or any other physical flow or resource. The CPS system comprises a distribution network for resource flow distribution and a computer network for real-time distribution control. Physical entities obey physical laws (for electric energy, Ohm's law and Kirchhoff's law), and can be monitored by means of measurement processes due to some fundamental properties that require monitoring and control in order to preserve over time the correct operation and stability of the system/network made up of said entities.
It is also known that the intensity of incompressible flows that enter a distribution network must remain stable along its entire path. For this reason, a measuring controller device must be able to monitor in real time the intensity of the flow, by comparing the values at various points of said network, especially at the input and output points thereof. One example is given by an alternating-current power distribution network using devices known as phasor measurement units (PMU) and phasor data concentrators (PDC), the main task of which is to ensure the decisional process carried out by a supervision and control (“Supervisory Control And Data Acquisition”, or SCADA) system.
Computer devices which are most commonly used in data acquisition systems for network control are PMUs and RTUs, or “Remote Terminal Units”. Such devices operate in real time but generate a lot of data, sending up to thirty packets per second and bringing the data traffic to a high, or even excessive, level.
For this reason, said PMU/PDC and RTU devices, though accurate in their measurements, cannot be employed in all network nodes (of the order of millions) because the data traffic would otherwise become so high as to be impossible to manage. Therefore, just a limited number of nodes, located on the main network backbones, are currently equipped with PMU devices, the use of which is planned according to the limits imposed by the communication network, while all other terminal nodes of the resource flow distribution network are equipped with a simple electronic meter that only carries out infrequent accounting operations not including any network control activities.
This technological limit explains why the terminations of resource distribution networks do not implement full monitoring and detection of the physical entities of the resource flow.
Typically the distribution network control centre performs a periodic scan (generally every 2-4 seconds) of the nodes equipped with real-time measuring devices in order to acquire the values of control parameters (energetic levels of the flow). The technical problem lies in the fact that circumstances which are relevant for control purposes (events) occur during a period that is much shorter than the scan cycle, thus not being visible to the control centre. Energy quality monitoring provides a special mode (called “sequence-of-event recording”) for recording the events that occur, which stores into the local memory of the measuring device all the history of what happened in the recent past. Even in the presence of this special mode, data sets may still not be immediately available to the central controller, often arriving late.
The availability of full monitoring of all remote nodes is of vital importance for intelligent networks to timely control the energy production from renewable sources in distributed mode. Unlike centralized generation, which is historically controllable via mechanisms known in the art, a flow of energy entering the network through a remote node that is only equipped with an electronic meter, i.e., without an RTU or a PMU, cannot be monitored at present.
Some mathematical models are also known for observing the effects of causes that may jeopardize the functionality and/or the stability of the distribution network. For example, in an alternating-current power distribution network, it is possible to measure the revolution speed (frequency) of power generators, which is reduced when energy consumption increases and is increased when consumption decreases. However, at present no technical solution exists that allows monitoring the final segments (the so-called “last mile”) of the low-voltage network. Several electronic devices for energy measurement exist which can communicate the meter readings, but none of such devices can operate in an event-driven mode by using the architectures of the EDA (“Event-Driven Architecture”) and/or SOA (“Service-Oriented Architecture”) systems. In most current solutions, the minimum limit is the sampling per second, and the data are available “upon request” via a scanning operation.
Indirect network control methods are based on a simple approach: wherever the network is stable no adjustment is made, while by continuously estimating the network's state it is possible to intervene should some actual measurements be different from the estimated values. In practice, the problem is attributable to the large number of points to be monitored and their respective positions.
The aim is to find a technologic solution with low computational complexity that allows the network topology to be directly and fully monitored.
It is also known that there are electronic devices for measuring flows of energy (sensors for electric energy, gas, water, etc.) that sample the flow with a high time resolution to obtain instantaneous values of physical entities, store them, and transmit them in the network at regular time intervals (“time-driven” method). In this manner, a large volume of data is exchanged between said devices and a central computer located in the same network. The measured quantity of energy E(t) can be expressed as the arithmetic sum of the single energy values Ei, i.e.:
      E    ⁡          (      t      )        ≈            ∑              i        =        0            t        ⁢                  ⁢          E      i      
For example, the instantaneous power values multiplied by a short observation time provide energy values that can be defined as “instantaneous”. As the index i changes, the accuracy of the method and the adherence of the series E(tk) to the series E(t) may vary significantly, introducing a measurement error ϵ(t)=|E(t)−ΣE(tk)|. Digital electronic devices for energy measurements utilize the discrete formula for calculating their output values.
The precision of the numerical calculation in the discrete space is preserved by a very high sampling frequency in the device (some MHz). Considering the limited quantity of internal memory of the device, only a few mean values aggregated during rather long time periods are stored and made externally available. Such values are also referred to as “pseudo-measurements”. An infinite numerical series of ever-increasing values {ETDM1, ETDM2, ETDM3, . . . , ETDMi} is thus obtained, where each ETDMi<ETDMi+1, represents the result of the measurement method known as “time-driven” method. In order to know just the quantity of energy exchanged during a certain period of time [t1, t2], two measurements E(t2) and E(t1) and the calculation procedure ΔE12=E(t2)−E(t1) are necessary and sufficient; the latter is typically used for invoicing.
Given the large number of meters in a distribution network (tens of millions or more) and the very high sampling frequency required for ensuring measurement accuracy, the “time-driven” method known in the art cannot be used by the network in real time because of the excessive volume of data that would otherwise be created. In order to reduce the data traffic, the meter accumulates the integral sums of the energy into its own internal memory, which task requires some time Δt=[ti−ti−1] that causes a natural delay in the communication with the network. For consolidating and communicating energy measurements in the network, a rather long time interval is therefore chosen, e.g., fifteen minutes. Depending on the implementation, which may vary according to the network's size, these “pseudo-measurements” may be (since they are averages) either transmitted immediately to the control computer or accumulated into the local memory of the measuring device and then transmitted in delayed mode, e.g., at the end of the day, week or month. Thus, some significant measurements may be lost, i.e., those indicating the actual energy distribution in the network; therefore, energy flow changes are not traced, while the network node equipped with such a meter remains unmonitored throughout the time period Δt. This makes it impossible to know the actual trend of the flow of energy, preventing a timely control of the entire network or a portion thereof.
According to the prior art, locally measured values can be transmitted in the network in two modes:                in “time-driven” mode with the series of measurements (“pseudo-measurements”) {ETDM1, ETDM2, ETDM3, . . . , ETDMi}, by using infrequent time sampling;        in “event-driven” mode with the series of measurements {EEDM1, EEDM2, EEDM3, . . . , ETDMi} in real time. In this case, these are actual measurements which are sent in compressed form upon every significant change in the flow, observing and eliminating any adjacent duplicates.        
This approach offers the advantage of reducing the amount of data transmitted in the network, thus making it possible to use meters for the same purposes as those offered by SCADA systems assisted by PMU-type devices. However, since it only provides differential values, there is a risk that some data packets will be lost, so that the period following the one during which the loss has occurred cannot be observed.